Literature DB >> 22618479

[Automated detection and volumetric segmentation of the spleen in CT scans].

M Hammon1, P Dankerl, M Kramer, S Seifert, A Tsymbal, M J Costa, R Janka, M Uder, A Cavallaro.   

Abstract

PURPOSE: To introduce automated detection and volumetric segmentation of the spleen in spiral CT scans with the THESEUS-MEDICO software. The consistency between automated volumetry (aV), estimated volume determination (eV) and manual volume segmentation (mV) was evaluated.
MATERIALS AND METHODS: Retrospective evaluation of the CAD system based on methods like "marginal space learning" and "boosting algorithms". 3 consecutive spiral CT scans (thoraco-abdominal; portal-venous contrast agent phase; 1 or 5 mm slice thickness) of 15 consecutive lymphoma patients were included. The eV: 30 cm³ + 0.58 (width × length × thickness of the spleen) and the mV as the reference standard were determined by an experienced radiologist.
RESULTS: The aV could be performed in all CT scans within 15.2 (± 2.4) seconds. The average splenic volume measured by aV was 268.21 ± 114.67 cm³ compared to 281.58 ± 130.21 cm³ in mV and 268.93 ± 104.60 cm³ in eV. The correlation coefficient was 0.99 (coefficient of determination (R²) = 0.98) for aV and mV, 0.91 (R² = 0.83) for mV and eV and 0.91 (R² = 0.82) for aV and eV. There was an almost perfect correlation of the changes in splenic volume measured with the new aV and mV (0.92; R² = 0.84), mV and eV (0.95; R² = 0.91) and aV and eV (0.83; R² = 0.69) between two time points.
CONCLUSION: The automated detection and volumetric segmentation software rapidly provides an accurate measurement of the splenic volume in CT scans. Knowledge about splenic volume and its change between two examinations provides valuable clinical information without effort for the radiologist. © Georg Thieme Verlag KG Stuttgart · New York.

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Year:  2012        PMID: 22618479     DOI: 10.1055/s-0031-1299495

Source DB:  PubMed          Journal:  Rofo        ISSN: 1438-9010


  6 in total

1.  Spleen volume on CT and the effect of abdominal trauma.

Authors:  Cinthia Cruz-Romero; Sheela Agarwal; Hani H Abujudeh; James Thrall; Peter F Hahn
Journal:  Emerg Radiol       Date:  2016-05-11

2.  Assessment of DICOM Viewers Capable of Loading Patient-specific 3D Models Obtained by Different Segmentation Platforms in the Operating Room.

Authors:  Giuseppe Lo Presti; Marina Carbone; Damiano Ciriaci; Daniele Aramini; Mauro Ferrari; Vincenzo Ferrari
Journal:  J Digit Imaging       Date:  2015-10       Impact factor: 4.056

3.  [Automatic segmentation and annotation in radiology].

Authors:  P Dankerl; A Cavallaro; M Uder; M Hammon
Journal:  Radiologe       Date:  2014-03       Impact factor: 0.635

4.  Efficient stereological approaches for the volumetry of a normal or enlarged spleen from MDCT images.

Authors:  Michalis Mazonakis; John Stratakis; John Damilakis
Journal:  Eur Radiol       Date:  2015-01-13       Impact factor: 5.315

5.  Assessing splenomegaly: automated volumetric analysis of the spleen.

Authors:  Marius George Linguraru; Jesse K Sandberg; Elizabeth C Jones; Ronald M Summers
Journal:  Acad Radiol       Date:  2013-03-25       Impact factor: 3.173

6.  Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning.

Authors:  Gabriel E Humpire-Mamani; Joris Bukala; Ernst T Scholten; Mathias Prokop; Bram van Ginneken; Colin Jacobs
Journal:  Radiol Artif Intell       Date:  2020-07-22
  6 in total

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